Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
481
datasets available to search
ShareScore release 0.9.0
Dataset results
481 results for “network model”
Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections - Supplementary videos
<p>We regard ROIs, regions of interest, from a human spleen specimen in single (four ROIs) and double (three ROIs) staining. The ROIs with the same number correspond to each other. Below we map references in manuscript (<strong>bold</strong>) to file names in this repository (<em>italics</em>).</p> <ul> <li>File <em>colour-deconvolution.png</em> – settings of colour deconvolution in Fiji for double staining.</li> <li>File <em>comments to videos.odt</em> – a commentary to S3[c,d] Video.</li> <li><strong>S1a,b Video to S3a,b Video</strong>: files <em>video_[1,2,3][a,b].mov</em> – sequence of section with single (a) and double (b) staining for ROI 1 to 3 in the main text.</li> <li><strong>S1c Video to S3c Video</strong>: files <em>video_[1,2,3]c.mov</em> – video of the reconstruction, single staining, special blood vessels highlighted.</li> <li><strong>S1d Video to S4d Video</strong>: files <em>video_[1,2,3]d.mov</em> – an overview video of the reconstruction, double staining.</li> <li><strong>S4 Video</strong>: file <em>video_4.mov</em> – quality control in virtual reality.</li> <li><strong>S1 Figure</strong>: a supplementary figure <em>fig_S1.tiff</em> and its caption <em>fig_S1_legend.odt</em></li> <li><strong>S2 Figure</strong>: a supplementary figure <em>fig_S2.tiff</em> and its caption <em>fig_S2_legend.odt</em></li> </ul> <p>This data corresponds to the publication "Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections" by B. S. Steiniger, C. Ulrich, M. Berthold, M. Guthe, and O. Lobachev, 2017.</p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"
<p>This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above.</p> <h2>Directory Structure</h2> <h3>`data`</h3> <p>Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper.</p> <h3>`eval`</h3> <p>Contains the predicted energies according to a MACE model for the following systems and facets:<br>- covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet<br>- evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet<br>- facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets.<br>- full: the model was trained on all facets and all coverages<br>- slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet<br>- Rh111: Energies for the Rh(111) + CHOH + CO systems.</p> <h3>`mcmc`</h3> <p>Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh<br>- copper-mcmc-public.tar.gz<br>- rhodium-mcmc-public.tar.gz</p> <h3>`models`</h3> <p>Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss:</p> <p>File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training.</p> <h3>`pyscripts`</h3> <p>Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`.</p> <h3>`scripts`</h3> <p>Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so.</p> <p>- Evaluation scripts (eval-*.sh)<br>- Training scripts (train-*.sh)</p> <h3>`train`</h3> <p>Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above.</p> <p>- Rh111<br>- covsplit<br>- evencov<br>- facet<br>- full<br>- slopes</p>
Daily observed, modelled, and infilled river flows for an Irish hydrometric reference network of river flow stations
<p>Here we present a dataset of observed, modelled, and infilled daily river flow data relating to the newly updated Irish Hydrometric Reference Network (IHRN) of high-quality gauging stations located across the Republic of Ireland. Internationally applied selection criteria, analysis of historical observations and flow gauge metadata, stakeholder feedback, and trend assessments aided in the identification of the network’s 51 stations. A combination of the GR4J conceptual hydrological model and a backpropagation neural network driven by catchment specific precipitation and temperature extracted from gridded datasets was used to model flows that subsequently infilled gaps in the observational record for each of the series (from commencement of each station’s record till the end of 2022). Also included are the 2.5 and 97.5 quantile values for each station’s modelled data, which represent the upper and lower uncertainty bounds of the respective ensemble flows derived during the flow generation process. As well as providing a useful means for evaluating the impact of changing climatic conditions on Irish catchments, the IHRN data offers utility for assessing catchment based impacts for flow extremes, and the generation of both historical reconstructions and future climate projections for a range of flow regimes across the island of Ireland.</p>
ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'
<p>ArrayCGH microarray images for 'Autoencoder and NCA based neural network model to estimate survival prognosis in multiple myeloma using arrayCGH data'</p>
Dataset for "Reproducibility and FAIR Principles: The Case of a Segment Polarity Network Model"
<p>Results of random sampling the segment polarity network with the simulator COPASI. These results correspond to Fig. 2 and Table 2 of von Dassow et. al (2000) (doi:10.1038/35018085). The random sampling was carried out with file vonDassow2000_1x4_alt.cps with COPASI version 4.39 selecting the appropriate parameter set named (1-7) and setting the number of repeats in the parameter scan task to the desired number. Full results of sampling are in files prefixed with the row number of Table 2 of von Dassow et. al (2000) and extension .tsv. Results with scores below 0.2 are in corresponding files with the word "-hits" in the filename. Includes also results from a time course simulation of this model using four different simulators (COPASI, Tellurium, Amici, and VCell). Finally also contains a study on multistability carried out by random sampling of parameters and initial conditions (run with COPASI). Markdown file README.md contains more detailed explanation. See also https://github.com/pmendes/models/tree/main/vonDassow2000</p>
Modeling CH4 and CO2 cycling using porewater stable isotopes in a thermokarst bog in Interior Alaska: Results from three conceptual reaction networks
Quantifying rates of microbial carbon transformation in peatlands is essential for gaining mechanistic understanding of the factors that influence methane emissions from these systems, and for predicting how emissions will respond to climate change and other disturbances. In this study, we used porewater stable isotopes collected from both the edge and center of a thermokarst bog in Interior Alaska to estimate in situ microbial reaction rates. We expected that near the edge of the thaw feature, actively thawing permafrost and greater abundance of sedges would increase carbon, oxygen and nutrient availability, enabling faster microbial rates relative to the center of the thaw feature. (full abstract available in supplemental file 610_NeumannPorewaterExtendedMetadataText.pdf)
Data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks"
<p>This upload contains the data and code related to the article "Characterization of the Log-normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks", (D.O.I: <a href="https://doi.org/10.3390/jsan9010012">10.3390/jsan9010012</a>) published in the the special issue on "Localization in Wireless Sensor Networks" of the <a href="https://www.mdpi.com/journal/jsan"><em>Journal of Sensor and Actuator Networks</em></a> (ISSN 2224-2708).</p> <p>The data and code included allows to replicate the results of the article.</p>
Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil
<p>Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics).</p> <p>The excel file includes Aphanomyces root rot disease visual scores (<em>Root_Rating</em>), unique identifier for each lentil accession/line (<em>Lentil_ID</em>), unique identifier for each experiment (<em>Experiment</em>), and unique identifier for each image (<em>Lab_ID</em>).</p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
A Queueing Network Model for Performance Prediction of Apache Cassandra
<p>The dataset consists in several csv files containing Cassandra and ScyllaDB performance.</p> <p>The experiments are organized in folders. There are three main folders containing:<br> - Cassandra 4 nodes: the files related to the Cassandra experiments conducted on a cluster composed of four nodes.<br> - ScyllaDB 4 nodes: The files related to the ScyllaDB experiments conducted on a cluster composed of four nodes. <br> - Cassandra QUORUM variant: the simulation data where a different kind of QUORUM is implemented in Cassandra.<br> <br> "Cassandra 4 nodes" and "ScyllaDB 4 nodes" include some subfolders, each one containing the files of the Consistency Level applied for those experiments. Each experiment is composed by three files (data*.csv) with the data reported by Yahoo! Cloud System Benchmark (YCSB) in the end of the experiment execution. Each folder contains also a sim.csv file with the data gathered from the simulation of the model inside Java Modeling Tool.</p> <p>The data*.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Number of Read requests<br> -Overall Read Response Time<br> -95 percentile Read Response Time<br> -99 percentile Read Response Time<br> -99.9 percentile Read Response Time<br> <br> Differently, the sim.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Overall Read Response Time</p>
Data package for paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events
<p>This is a data package accompanying the paper "DeepGlow: an efficient neural-network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events".</p>
Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model
<p><span>Kelp forests are important marine ecosystems providing habitat for numerous species. Despite over 50 years of mechanical harvesting in the Northeast Atlantic, the indirect impacts of kelp harvesting and associated habitat loss on faunal species within kelp forests remain poorly understood. We investigated the consequences of kelp harvesting by developing an allometric trophic network model for a subtidal Northeast Atlantic kelp forest (dominated by <em>Laminaria</em> <em>hyperborea</em>). Additionally, we designed a novel mechanistic model to explore the non-trophic interactions between kelp and age class 0 Atlantic cod (<em>Gadus</em> <em>morhua</em>) and kelp and European lobster (<em>Homarus</em> <em>gammarus</em>), specifically focusing on the increased survival benefits provided by the kelp habitat. Simulations were conducted over a 50-year period, incorporating harvesting cycles of 2, 5, and 9 years, as well as low and high harvesting intensities. Our findings reveal the complex dynamics resulting from kelp harvesting. The recovery of kelp biomass was observed with 5- and 9-year harvesting cycles, whereas a decline was observed with a 2-year cycle. Furthermore, the non-trophic interaction facilitated a higher pre-harvest biomass for both the European lobster and the Atlantic cod compared to scenarios without this interaction. These results highlight the multitrophic effects of kelp harvesting and emphasize that the recovery of kelp-associated species may not necessarily align with kelp recovery, depending on harvesting intensity and recovery periods. Importantly, our study contributes to a better understanding of the ecological consequences of kelp harvesting and underscores the need for sustainable management practices to mitigate habitat loss in kelp ecosystems.</span></p>
Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements generated with either character writing (PCR) or with 3D target reaching using reinforcement learning (RL).</p> <p>The overall structure of the data is:</p> <p>└── spindle_datasets<br> ├── pcr_dataset - Contains PCR synthetic training dataset<br> └── rl_dataset - Contains RL-generated synthetic training dataset</p> <p>The code to generate the PCR synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation</a></p> <p>The code to generate the RL-generated synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the trained model checkpoints for all tasks of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains 300 temporal convolutional networks (TCNs) and 50 LSTM models trained on 16 tasks as well as the untrained initialization. </p> <p>The overall structure of the data is:</p> <p>└── models<br> ├── deepdraw_models - Contains networks hyperparameters<br> │ ├── template_models - Contains the default parameters<br> │ ├── torque - Contains network hyperparameters for the torque task<br> │ └── ... <br> ├── experiment_*** - Contains checkpoint of trained and untrained models <br> ├── ... <br> └── ... </p> <p>--------------------------------</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction - task transfer (AR): 10020, 10035, 10050<br>- Redundancy reduction - task transfer (HP): 10021, 10036, 10051<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p>The code to load, evaluate and train the models is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/nn-training">https://github.com/amathislab/Task-driven-Proprioception/tree/master/nn-training</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article:</p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception".</p> <p>It contains the behavioral and neural experimental data (cuneate nucleus and somatosensory recordings from the Miller Lab, Northwestern University), the result dataframes for task-driven and untrained models, the activations and predictions for the *best models for all tasks* for active and passive movements and the predictions for linear models for active and passive movements. </p> <p>Note, the predictions of other models can be computed from the network weights that were deposited for all trained models. </p> <p>The overall structure of the data is:</p> <p>└── exp_analysis<br> ├── results - Contains the result dataframe of the predictions for all models, tasks and primates<br> ├── activations<br> │ ├── active - Contains activations related to active movements<br> │ └── passive - Contains activations related to passive movements<br> ├── predictions<br> │ ├── active - Contains predictions related to active movements<br> │ └── passive - Contains predictions related to passive movements<br> └── beh_exp_datasets<br> ├── matlab_data - Contains raw behavioral and neural data<br> ├── MonkeyAlignedDatasets_new - Contains padded test behavioral input for generating network activations<br> ├── MonkeyDatasets - Contains not aligned padded test behavioral input for generating network activations<br> ├── MonkeySpikeRegressDatasets - Contains datasets for training data-driven models<br> ├── MonkeySpikeRegressDatasets_new - Contains trial index for regression splits <br> └── new_beh_exp_dataframe - Contains pre-processed behavioral and neural data</p> <p>--------------------------------</p> <p>The activations and predictions for the best 3 models and for all tasks are stored in experiments folder (in .h5 format) that follows the same name convention of the checkpoints.</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction: 10020, 10035, 10050<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p> </p> <p>The code to process the behavioral data is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing">https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing</a><br>The code to load and use the models to generate activations and predictions is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction">https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction</a></p> <p>To reproduce the results, it is possible to reproduce the main figures using the result dataframe. See our repository for more details. </p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Data for "Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada"
<p><a name="_Toc157072893"></a><strong>Abstract</strong>. This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020-2023. Precipitation phase observations showed a 2-m air temperature interval between 0-4°C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5°C, a linear transition model with dual temperature thresholds of –0.38 and 5°C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. In terms of partitioning error, PGP_full had the lowest RMSE and the least variability in performance. The RMSE of the single temperature threshold model was the highest and showed the greatest performance variability. The improvement of mixed-phase prediction remains a challenge. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models.</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks
<p>This dataset comes from the following paper:</p> <p>Matteo Arricca, Nicoletta Inverardi, Stefano Pandini, Maurizio Toselli, Massimo Messori, Giulia Scalet, Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks, Journal of the Mechanics and Physics of Solids, 105955, 2024. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105955" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/j.jmps.2024.105955</span></span></a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. txt experimental data</li> </ul>
GNN Models and results for the paper "Band-gap regression with architecture-optimized message-passing neural networks"
<p>Contains files with model parameters for random search and reference models, as well as the converted AFLOW dataset, in graphs form. Corresponds to results in <a href="https://arxiv.org/pdf/2309.06348.pdf">https://arxiv.org/pdf/2309.06348.pdf</a>.</p> <p>Model predictions along with AUID identifiers are located in result_combined.zip, band gap (egap) and formation energy (ef) predictions are from the PaiNN ensemble, band gap classification is done by MPEU model.</p> <p>New results include PaiNN NAS models.</p> <p>Compatible source code can be found at <a href="https://github.com/tisabe/jraph_MPEU/tree/v1.0.0">jraph_MPEU GitHub repository</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.